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Civil-Comp Proceedings
ISSN 1759-3433 CCP: 102
PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by:
Paper 207
Rare Event Sampling using the Science Experimental Grid Laboratory Y. Dorozhko1, K. Kratzer2, Y. Yudin1, A. Arnold2, C.W. Glass1 and M. Resch1
1High Performance Computing Center Stuttgart (HLRS)
Y. Dorozhko, K. Kratzer, Y. Yudin, A. Arnold, C.W. Glass, M. Resch, "Rare Event Sampling using the Science Experimental Grid Laboratory", in , (Editors), "Proceedings of the Fourteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 207, 2013. doi:10.4203/ccp.102.207
Keywords: SEGL, GriCoL, workflow, workflow management, forward flux sampling, FFS, rare event sampling, HPC, control flow, data flow, simulation technologies, workflow languages.
Summary
Rare events are difficult to simulate because of the long waiting times between their
occurrences. This can be overcome by rare event sampling techniques, which allow
one to push the system towards the interesting event without biasing the system.
However, the implementation of these techniques, that are added on top of a conventional
simulation tool, on high performance computers in parallel is a difficult task.
In this paper, we describe how one of these sampling techniques, namely forward
flux sampling (FFS), can be implemented as a workflow application in the context
of the Science Experimental Grid Laboratory (SEGL). SEGL handles the workflow
and dataflow of the simulation and matches the requirements for FFS by drawing a
certain number of random datasets from a given dataspace. We demonstrate the usability
of this approach on an example simulation problem from physics. FFS can
easily be used together with any simulation software which can be described in the
context of the SEGL framework, without the need for implementing the method by
the researcher, making rare event simulations easily available. Beyond that, trajectory
fragments can be calculated in parallel and on high performance computing hardware
by design.
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